Reference : On Revision of Partially Specified Convex Probabilistic Belief Bases

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/30950

Title :	On Revision of Partially Specified Convex Probabilistic Belief Bases
Language :	English
Author, co-author :	Rens, Gavin [University of Cape Town > Computer Science]
	Meyer, Thomas [University of Cape Town > Computer Science]
	Casini, Giovanni [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2016
Main document title :	Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI-16)
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-61499-671-2
Event name :	The 22nd European Conference on Artificial Intelligence (ECAI-16)
Event date :	29/08/2016 - 02/09/2016
Event place (city) :	the Hague
Event country :	Holland
Keywords :	[en] Probability ; Belief Revision
Abstract :	[en] We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy methods are reasonable, yet yield different results.
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/30950
FnR project :	FnR ; FNR9181001 > Giovanni Casini > SOUL > Subjective and Objective Uncertainty in Description Logics > 01/07/2015 > 30/06/2017 > 2014

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